deposition_locations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import math\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots describing deposition locations of publications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PublicationTitle</th>\n",
       "      <th>DatabaseIDs</th>\n",
       "      <th>PublicationDate</th>\n",
       "      <th>PublicationDOI</th>\n",
       "      <th>StudyType</th>\n",
       "      <th>SegmentationUse</th>\n",
       "      <th>SourceData</th>\n",
       "      <th>TrainingData</th>\n",
       "      <th>LabelData</th>\n",
       "      <th>Code</th>\n",
       "      <th>Modality</th>\n",
       "      <th>FileFormatsSource</th>\n",
       "      <th>FileFormatsTraining</th>\n",
       "      <th>FileFormatsLabel</th>\n",
       "      <th>DataOrigin</th>\n",
       "      <th>SegmentationType</th>\n",
       "      <th>SegmentationMethod</th>\n",
       "      <th>BiologicalScale</th>\n",
       "      <th>Notes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A novel framework for segmentation of secretor...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2014</td>\n",
       "      <td>10.1016/j.media.2013.12.008</td>\n",
       "      <td>Software</td>\n",
       "      <td>Proof-of-concept</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>TEM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>this study</td>\n",
       "      <td>Semantic</td>\n",
       "      <td>Automated</td>\n",
       "      <td>Subcellular</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cryo-FIB-SEM serial milling and block face ima...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>10.1016/j.jsb.2016.09.016</td>\n",
       "      <td>Method</td>\n",
       "      <td>Qualitative visualisation</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Cryo-FIB-SEM</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>this study</td>\n",
       "      <td>Semantic</td>\n",
       "      <td>Automated</td>\n",
       "      <td>Subcellular</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Convolutional neural networks for automated an...</td>\n",
       "      <td>EMD-8594, EMD-8589, EMD-8590, EMD-8591, EMD-85...</td>\n",
       "      <td>2017</td>\n",
       "      <td>10.1038/nmeth.4405</td>\n",
       "      <td>Software</td>\n",
       "      <td>STA</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EMDB</td>\n",
       "      <td>Github</td>\n",
       "      <td>Cryo-ET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>map</td>\n",
       "      <td>this study, public data</td>\n",
       "      <td>Instance</td>\n",
       "      <td>Automated</td>\n",
       "      <td>Subcellular</td>\n",
       "      <td>Code integrated into existing software EMAN2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Extracellular space preservation aids the conn...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>10.7554/eLife.08206</td>\n",
       "      <td>Method</td>\n",
       "      <td>Proof-of-concept</td>\n",
       "      <td>Dryad</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>SBF SEM</td>\n",
       "      <td>zipped</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>this study</td>\n",
       "      <td>Instance</td>\n",
       "      <td>Automated</td>\n",
       "      <td>Tissue (connectomics)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Domain adaptation for microscopy imaging</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015</td>\n",
       "      <td>10.1109/TMI.2014.2376872</td>\n",
       "      <td>Software</td>\n",
       "      <td>Proof-of-concept</td>\n",
       "      <td>Some (DIADEM)</td>\n",
       "      <td>Some (DIADEM)</td>\n",
       "      <td>No</td>\n",
       "      <td>Institutional - working</td>\n",
       "      <td>3D EM</td>\n",
       "      <td>tiff</td>\n",
       "      <td>swc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>this study</td>\n",
       "      <td>Semantic</td>\n",
       "      <td>Automated</td>\n",
       "      <td>Subcellular</td>\n",
       "      <td>Need to find code from reference to another pa...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    PublicationTitle  \\\n",
       "0  A novel framework for segmentation of secretor...   \n",
       "1  Cryo-FIB-SEM serial milling and block face ima...   \n",
       "2  Convolutional neural networks for automated an...   \n",
       "3  Extracellular space preservation aids the conn...   \n",
       "4           Domain adaptation for microscopy imaging   \n",
       "\n",
       "                                         DatabaseIDs  PublicationDate  \\\n",
       "0                                                NaN             2014   \n",
       "1                                                NaN             2016   \n",
       "2  EMD-8594, EMD-8589, EMD-8590, EMD-8591, EMD-85...             2017   \n",
       "3                                                NaN             2015   \n",
       "4                                                NaN             2015   \n",
       "\n",
       "                PublicationDOI StudyType            SegmentationUse  \\\n",
       "0  10.1016/j.media.2013.12.008  Software           Proof-of-concept   \n",
       "1    10.1016/j.jsb.2016.09.016    Method  Qualitative visualisation   \n",
       "2           10.1038/nmeth.4405  Software                        STA   \n",
       "3          10.7554/eLife.08206    Method           Proof-of-concept   \n",
       "4     10.1109/TMI.2014.2376872  Software           Proof-of-concept   \n",
       "\n",
       "      SourceData   TrainingData LabelData                     Code  \\\n",
       "0             No            NaN        No                       No   \n",
       "1             No            NaN        No                       No   \n",
       "2             No            NaN      EMDB                   Github   \n",
       "3          Dryad             No        No                       No   \n",
       "4  Some (DIADEM)  Some (DIADEM)        No  Institutional - working   \n",
       "\n",
       "       Modality FileFormatsSource FileFormatsTraining FileFormatsLabel  \\\n",
       "0           TEM               NaN                 NaN              NaN   \n",
       "1  Cryo-FIB-SEM               NaN                 NaN              NaN   \n",
       "2       Cryo-ET               NaN                 NaN              map   \n",
       "3       SBF SEM            zipped                 NaN              NaN   \n",
       "4         3D EM              tiff                 swc              NaN   \n",
       "\n",
       "                DataOrigin SegmentationType SegmentationMethod  \\\n",
       "0               this study         Semantic          Automated   \n",
       "1               this study         Semantic          Automated   \n",
       "2  this study, public data         Instance          Automated   \n",
       "3               this study         Instance          Automated   \n",
       "4               this study         Semantic          Automated   \n",
       "\n",
       "         BiologicalScale                                              Notes  \n",
       "0            Subcellular                                                NaN  \n",
       "1            Subcellular                                                NaN  \n",
       "2            Subcellular       Code integrated into existing software EMAN2  \n",
       "3  Tissue (connectomics)                                                NaN  \n",
       "4            Subcellular  Need to find code from reference to another pa...  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import csv of review data\n",
    "df = pd.read_csv(\"FAIRly_Depositing_Segmentations.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boilerplate\n",
    "\n",
    "def count_each_category(df_series, lookup_dict):\n",
    "    \"\"\"Counts records within a specific category defined by lookup dict\n",
    "    e.g., if we want to count all records with public challenges as the data source,\n",
    "    then lookup_dict should list all the data sources which count as public challenges.\n",
    "\n",
    "    Args:\n",
    "        df_series (pd.Series): records to look through, e.g., df[TrainingData]\n",
    "        lookup_dict (_type_): _description_\n",
    "\n",
    "    Returns:\n",
    "        _type_: _description_\n",
    "    \"\"\"\n",
    "    category_counts = {}\n",
    "    for category in set(lookup_dict.values()):\n",
    "        category_counts[category] = 0\n",
    "    for i in df_series:\n",
    "        if pd.isna(i):\n",
    "            pass\n",
    "        else:\n",
    "            for value, category in lookup_dict.items():\n",
    "                if value.lower() in i.lower():\n",
    "                    category_counts[category] += 1\n",
    "    return category_counts\n",
    "\n",
    "def subplot_bar(data, ax, colour, y):\n",
    "    sns.barplot(data=data, x=\"Percentage\", y=y, hue=y, ax=ax, palette=colour, legend=False)\n",
    "    labels = []\n",
    "    for i in range(len(data)):\n",
    "        if data.iloc[i].Count > 0:\n",
    "            labels.append(f\"{data.iloc[i].Percentage:.1f}%, {data.iloc[i].Count:.0f}\")\n",
    "        else:\n",
    "            labels.append(\"\")\n",
    "    for i, label in enumerate(labels):\n",
    "        ax.text(2, i, label)\n",
    "\n",
    "\n",
    "def plot_dep_locations(deposition_category_counts_df, title=None):\n",
    "\n",
    "    sns.set_style(\"whitegrid\")\n",
    "    fig, axs = plt.subplots(3, 4, sharex=True, height_ratios=[2,3,7], figsize=(8,11), subplot_kw={\"xticklabels\": \"\"})\n",
    "\n",
    "    colours = [\"light:red_r\", \"light:orange_r\", \"light:mediumseagreen_r\"]\n",
    "    for i, datatype in enumerate([\"SourceData\", \"TrainingData\", \"LabelData\", \"Code\"]):\n",
    "        for category in deposition_category_counts_df.DepositionFA:\n",
    "            data = deposition_category_counts_df[(deposition_category_counts_df.DataType == datatype) & (deposition_category_counts_df.DepositionFA == category)]\n",
    "            subplot_bar(data, axs[category, i], colours[category], y=\"RepositoryType\")\n",
    "\n",
    "    titles = [\"Not findable or accessible\\n\", \"Not findable\", \"Findable and accessible\"]\n",
    "    datatypes = [\"Source Data\", \"Training Data\", \"Label Data\", \"Code\"]\n",
    "    for i in range(3):\n",
    "        for j in range(4):\n",
    "            axs[i,j].set_xlabel(\"\")\n",
    "            axs[i,j].set_ylabel(\"\")\n",
    "            if j > 0:\n",
    "                axs[i,j].set_yticklabels(\"\")\n",
    "                axs[i,j].set_title(\"\")\n",
    "                axs[0,j].set_title(datatypes[j])\n",
    "            axs[i,0].set_title(titles[i], position=(-1.3,0), ha=\"left\", fontweight=\"bold\", linespacing=3, fontsize=\"x-large\")\n",
    "    fig.text(0.275,0.928,\"Source Data\", fontsize=\"large\")\n",
    "    # plt.suptitle(title)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.savefig(f\"./figs/{title}_deplocations.svg\", transparent=True, dpi=200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Categories of deposition location\n",
    "\n",
    "source_data_dep = {\n",
    "    'BossDB': 'General scientific database',\n",
    "    'CREMI': 'Public challenge',\n",
    "    'Dryad': 'General scientific database',\n",
    "    'EMDB': 'Field-specific database',\n",
    "    'Kaggle': 'Public challenge',\n",
    "    'EMPIAR': 'Field-specific database',\n",
    "    'Bioimage Archive': 'Field-specific database',\n",
    "    'Institutional - working': 'Self-maintained website',\n",
    "    'GigaScience': 'General scientific database',\n",
    "    'Github': 'Self-maintained website',\n",
    "    'Google Drive, MEGA, Baidu Cloud': 'Commercial cloud',\n",
    "    'Institutional - defunct': 'Not available anymore',\n",
    "    'Mendeley': 'General scientific database',\n",
    "    'MorphoSource': 'Field-specific database',\n",
    "    'Neurodata': 'Field-specific database',\n",
    "    'No': 'Not deposited',\n",
    "    'Not anymore': 'Not available anymore',\n",
    "    'Open Science Foundation': 'General scientific database',\n",
    "    'ScanAllFish': 'Field-specific database',\n",
    "    'White Matter Microscopy Database': 'Field-specific database',\n",
    "    'OpenOrganelle': 'Field-specific database',\n",
    "    'Own website - defunct': 'Not available anymore',\n",
    "    'References other paper': 'References other paper',\n",
    "    'Requires separate search (Cell-centred database)': 'Requires separate search',\n",
    "    'Cell-centred database': 'Field-specific database',\n",
    "    'Requires separate search': 'Requires separate search',\n",
    "    'ISBI2012': 'Public challenge',\n",
    "    'SNEMI3D': 'Public challenge',\n",
    "    'SHREC': 'Public challenge',\n",
    "    'SPARC': 'Field-specific database',\n",
    "    'Cell Image Library': 'Field-specific database',\n",
    "    'DIADEM': 'Public challenge',\n",
    "    'Zenodo': 'General scientific database',\n",
    "}\n",
    "\n",
    "training_data_dep = {\n",
    "    'CREMI': 'Public challenge',\n",
    "    'EMPIAR': 'Field-specific database',\n",
    "    'Github': 'Self-maintained website',\n",
    "    'Google Drive, MEGA, Baidu Cloud': 'Commercial cloud',\n",
    "    'Institutional - defunct': 'Not available anymore',\n",
    "    'Institutional - working': 'Self-maintained website',\n",
    "    'Neurodata': 'Field-specific database',\n",
    "    'No': 'Not deposited',\n",
    "    'OpenOrganelle': 'Field-specific database',\n",
    "    'Requires separate search': 'Requires separate search',\n",
    "    'ISBI2012': 'Public challenge',\n",
    "    'SNEMI3D': 'Public challenge',\n",
    "    'SHREC': 'Public challenge',\n",
    "    'SPARC': 'Field-specific database',\n",
    "    'DIADEM': 'Public challenge',\n",
    "    'Zenodo': 'General scientific database',\n",
    "}\n",
    "\n",
    "label_data_dep = {\n",
    "    'BossDB': 'General scientific database',\n",
    "    'CZII Cryo ET Data Portal': 'Field-specific database',\n",
    "    'DIY': 'DIY',\n",
    "    'Dryad': 'General scientific database',\n",
    "    'EMDB': 'Field-specific database',\n",
    "    'EMPIAR': 'Field-specific database',\n",
    "    'GigaScience': 'General scientific database',\n",
    "    'Github': 'Self-maintained website',\n",
    "    'Institutional - defunct': 'Not available anymore',\n",
    "    'Institutional - working': 'Self-maintained website',\n",
    "    'Mendeley': 'General scientific database',\n",
    "    'Mobie': 'Field-specific database',\n",
    "    'Neurodata': 'Field-specific database',\n",
    "    'No': 'Not deposited',\n",
    "    'Open Science Foundation': 'General scientific database',\n",
    "    'ScanAllFish': 'Field-specific database',\n",
    "    'OpenOrganelle': 'Field-specific database',\n",
    "    'Requires separate search': 'Requires separate search',\n",
    "    'ISBI2012': 'Public challenge',\n",
    "    'SNEMI3D': 'Public challenge',\n",
    "    'SPARC': 'Field-specific database',\n",
    "    'Supplementary Info': 'Supplementary Info',\n",
    "    'Zenodo': 'General scientific database',\n",
    "}\n",
    "\n",
    "code_dep = {\n",
    "    'Github': 'Self-maintained website',\n",
    "    'Zenodo': 'General scientific database',\n",
    "    'Gitlab': 'Self-maintained website',\n",
    "    'ImageJ plugin': 'In other software',\n",
    "    'Institutional - defunct': 'Not available anymore',\n",
    "    'Institutional - working': 'Self-maintained website',\n",
    "    'Maybe, references another paper for method': 'References other paper',\n",
    "    'Mendeley': 'General scientific database',\n",
    "    'Might be part of Ilastik, but unsure.': 'In other software',\n",
    "    'No': 'Not deposited',\n",
    "    'Not anymore (ImageJ website)': 'Not available anymore',\n",
    "    'Supplementary Info': 'Supplementary Info',\n",
    "    'Website - working': 'Self-maintained website',\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RepositoryType</th>\n",
       "      <th>DataType</th>\n",
       "      <th>Count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Field-specific database</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not available anymore</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Public challenge</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>References other paper</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Commercial cloud</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RepositoryType    DataType  Count\n",
       "0  Field-specific database  SourceData   55.0\n",
       "1    Not available anymore  SourceData    4.0\n",
       "2         Public challenge  SourceData   20.0\n",
       "3   References other paper  SourceData    3.0\n",
       "4         Commercial cloud  SourceData    1.0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort data deposition locations into categories and count\n",
    "\n",
    "deposition_category_counts_df = pd.DataFrame.from_dict({\n",
    "    \"SourceData\": count_each_category(df.SourceData, source_data_dep),\n",
    "    \"TrainingData\": count_each_category(df.TrainingData, training_data_dep),\n",
    "    \"LabelData\": count_each_category(df.LabelData, label_data_dep),\n",
    "    \"Code\": count_each_category(df.Code, code_dep)\n",
    "})\n",
    "\n",
    "deposition_category_counts_df[\"RepositoryType\"] = deposition_category_counts_df.index\n",
    "\n",
    "deposition_category_counts_df = deposition_category_counts_df.melt(\n",
    "    id_vars=\"RepositoryType\",\n",
    "    value_vars=[\"SourceData\", \"TrainingData\", \"LabelData\", \"Code\"],\n",
    "    var_name=\"DataType\",\n",
    "    value_name=\"Count\"\n",
    ")\n",
    "\n",
    "deposition_category_counts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RepositoryType</th>\n",
       "      <th>DataType</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percentage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Field-specific database</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>55.0</td>\n",
       "      <td>24.229075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not available anymore</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.762115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Public challenge</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.810573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>References other paper</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.321586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Commercial cloud</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.440529</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RepositoryType    DataType  Count  Percentage\n",
       "0  Field-specific database  SourceData   55.0   24.229075\n",
       "1    Not available anymore  SourceData    4.0    1.762115\n",
       "2         Public challenge  SourceData   20.0    8.810573\n",
       "3   References other paper  SourceData    3.0    1.321586\n",
       "4         Commercial cloud  SourceData    1.0    0.440529"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add percentages\n",
    "\n",
    "# datatype_total_counts = number of non N/A publications for each data type\n",
    "# need to count separately as deposition_category_counts_df has duplicates for where data is sourced from multiple locations\n",
    "datatype_total_counts = {\n",
    "    \"SourceData\": df.SourceData.count().sum(),\n",
    "    \"TrainingData\": df.TrainingData.count().sum(),\n",
    "    \"LabelData\": df.LabelData.count().sum(),\n",
    "    \"Code\": df.Code.count().sum(),\n",
    "}\n",
    "pct = []\n",
    "for i in range(len(deposition_category_counts_df)):\n",
    "    pct.append(100 * deposition_category_counts_df.iloc[i].Count / datatype_total_counts[deposition_category_counts_df.iloc[i].DataType])\n",
    "\n",
    "deposition_category_counts_df[\"Percentage\"] = pct\n",
    "deposition_category_counts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RepositoryType</th>\n",
       "      <th>DataType</th>\n",
       "      <th>Count</th>\n",
       "      <th>Percentage</th>\n",
       "      <th>DepositionFA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Field-specific database</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>55.0</td>\n",
       "      <td>24.229075</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Not available anymore</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.762115</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Public challenge</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>20.0</td>\n",
       "      <td>8.810573</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>References other paper</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.321586</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Commercial cloud</td>\n",
       "      <td>SourceData</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.440529</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            RepositoryType    DataType  Count  Percentage  DepositionFA\n",
       "0  Field-specific database  SourceData   55.0   24.229075             2\n",
       "1    Not available anymore  SourceData    4.0    1.762115             0\n",
       "2         Public challenge  SourceData   20.0    8.810573             2\n",
       "3   References other paper  SourceData    3.0    1.321586             1\n",
       "4         Commercial cloud  SourceData    1.0    0.440529             1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorise according to the following categories of deposition locations:\n",
    "# 0 - not findable or accessible\n",
    "# 1 - not findable\n",
    "# 2 - findable and accessible\n",
    "\n",
    "deposition_FA = []\n",
    "for i in range(len(deposition_category_counts_df)):\n",
    "    if deposition_category_counts_df.iloc[i].RepositoryType in [\"Not deposited\", \"Not available anymore\", \"Not available yet\"]:\n",
    "        deposition_FA.append(0)  # not findable or accessible\n",
    "    elif deposition_category_counts_df.iloc[i].RepositoryType in [\"Requires separate search\", \"References other paper\", \"Commercial cloud\"]:\n",
    "        deposition_FA.append(1)  # not findable\n",
    "    else:\n",
    "        deposition_FA.append(2)  # everything else\n",
    "\n",
    "deposition_category_counts_df[\"DepositionFA\"] = deposition_FA\n",
    "\n",
    "deposition_category_counts_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x1100 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot for all data\n",
    "\n",
    "plot_dep_locations(deposition_category_counts_df, \"all\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "analysis",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}